SAN FRANCISCO, CA – StreetLight Data, Inc, the leader in Big Data analytics for mobility, today launched Annual Average Daily Traffic (AADT) metrics for Canada on the StreetLight InSight® platform. Providing the most accurate, on-demand traffic volumes for over 4.5 million miles of Canadian and U.S. roadway spanning 2018 and 2017, the company’s AADT metric enables smarter cities and better infrastructure decisions.

AADT is a critical metric for transportation planners and engineers analyzing infrastructure projects, estimating road safety, or seeking highway funds. Historically, AADT counts have been measured manually and at a high cost. The process of getting approval, training staff, collecting actual counts, and validating the data can typically take months and cost hundreds of thousands of dollars.

With the StreetLight Insight platform, users can query the software for accurate AADT counts for virtually every Canadian and U.S. road, with results delivered in minutes.

“We recognize the challenges of securing accurate road counts for one of the largest but most sparsely populated nations in the world,” said Laura Schewel, CEO and co-founder of StreetLight Data. “With our AADT Canada release, we can now bring complete traffic data sets to planners covering both Canada’s largest cities and extensive rural areas.”

Trained and tested using more than 9,100 and 1,800 permanent loop counts respectively in the U.S. and Canada, StreetLight’s AADT can be rendered for bi-directional traffic, or zero in on traffic moving in one direction on a roadway, including ramps, freeway-to-freeway connectors, and local/minor roads. Each analysis also includes a “confidence range” (or prediction interval) for the metrics provided.

Based on over 100 billion monthly location records across Canada and the U.S., StreetLight’s algorithms draw on 365 days of data on more than 4.5 million miles of roadway. Available for urban and rural roads large and small, the company’s U.S. and Canada AADT counts considerably outperform industry-standard accuracy targets.